Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding

Overview

⚠️ Checkout develop branch to see what is coming in pyannote.audio 2.0:

Neural speaker diarization with pyannote-audio

pyannote.audio is an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it provides a set of trainable end-to-end neural building blocks that can be combined and jointly optimized to build speaker diarization pipelines:

pyannote.audio also comes with pretrained models covering a wide range of domains for voice activity detection, speaker change detection, overlapped speech detection, and speaker embedding:

segmentation

Open In Colab

Installation

pyannote.audio only supports Python 3.7 (or later) on Linux and macOS. It might work on Windows but there is no garantee that it does, nor any plan to add official support for Windows.

The instructions below assume that pytorch has been installed using the instructions from https://pytorch.org.

$ pip install pyannote.audio==1.1.1

Documentation and tutorials

Until a proper documentation is released, note that part of the API is described in this tutorial.

Citation

If you use pyannote.audio please use the following citation

@inproceedings{Bredin2020,
  Title = {{pyannote.audio: neural building blocks for speaker diarization}},
  Author = {{Bredin}, Herv{\'e} and {Yin}, Ruiqing and {Coria}, Juan Manuel and {Gelly}, Gregory and {Korshunov}, Pavel and {Lavechin}, Marvin and {Fustes}, Diego and {Titeux}, Hadrien and {Bouaziz}, Wassim and {Gill}, Marie-Philippe},
  Booktitle = {ICASSP 2020, IEEE International Conference on Acoustics, Speech, and Signal Processing},
  Address = {Barcelona, Spain},
  Month = {May},
  Year = {2020},
}
Comments
  • [WIP] Multilabel Detection

    [WIP] Multilabel Detection

    This is a new PR for the VTC feature, this time based on a cleaner implem. I'm making a new PR as to keep the former branch "clean" (and prevent any mishaps).

    What is done:

    • renaming the SpeakerTracking task into a MultilabelDetection task
    • added MultilabelPipeline
    • update MultilabelFscore.report()
    • tested the new preprocessor

    What's to be done:

    • [ ] re-test the new implem on our clinical data (as well as the child data from @MarvinLvn )
    • [ ] maybe a couple of unit tests (especially for the preprocessor)
    • [ ] maybe make the aggregated "multilabel" fscore duration-based instead of file-based
    opened by hadware 29
  • Trying the diarization pipeline on random .wav files

    Trying the diarization pipeline on random .wav files

    Hey, as suggested by the detailed tutorials, i went through them and trained all the models required for the pipeline. The pipeline is working on the AMI dataset but when i try to reproduce the results on other .wav files sampled at 16k, mono, and 256bps, it is not able to diarize the audio. Here is the breif of what i actually did.

    1. Took a random meeting audio file, sampled at 16k , mono and 256bps
    2. renamed it to ES2003a and replaced it with actual ES2003a ( thought it as a turnaround of creating another database )
    3. ran all the pipelines ( sad,scd, emb, diarization )

    Output :

    1. Speaker activity detection works perfectly and is able to classify regions of speech.
    2. Speaker diarization does't works, everything is classified as 0

    can you please tell if its because of replacing the actual file that the pipeline is giving wrong outputs for the diarization, and whats a better way to test the pipeline on random audios.

    opened by saisumit 26
  • build error

    build error

    Hi, when I run pip install "pyannote.audio==0.3", I got the following error msg:

    In file included from _pysndfile.cpp:471:0: pysndfile.hh:55:21: fatal error: sndfile.h: No such file or directory #include <sndfile.h> ^ compilation terminated. error: command 'gcc' failed with exit status 1


    Failed building wheel for pysndfile Running setup.py clean for pysndfile Failed to build pysndfile

    cannot_reproduce 
    opened by ChristopherLu 24
  • Add support for file handle to pyannote.audio.core.io.Audio

    Add support for file handle to pyannote.audio.core.io.Audio

    This is not currently supported:

    from pyannote.audio.core.io import Audio
    from pyannote.core import Segment
    audio = Audio()
    with open('file.wav', 'rb') as f:
        waveform, sample_rate = audio(f)
    with open('file.wav', 'rb') as f:
        waveform, sample_rate = audio.crop(f, Segment(10, 20))
    

    One has to do this instead:

    from pyannote.audio.core.io import Audio
    from pyannote.core import Segment
    audio = Audio()
    waveform, sample_rate = audio('file.wav')
    waveform, sample_rate = audio.crop('file.wav', Segment(10, 20))
    

    This is a limitation that might be problematic (e.g. with streamlit.file_uploader that returns a file handle)

    v2 
    opened by hbredin 20
  • ValueError: inconsistent

    ValueError: inconsistent "classes" (is ['non_change', 'change'], should be: ['non_speech', 'speech'])

    Describe the bug I'm trying to go through the diarization pipeline tutorial on my own data.

    I am trying to run "apply" on my own data and model for speaker change detection. I get an error that looks like it's trying to apply speech activity detection

    ValueError: inconsistent "classes" (is ['non_change', 'change'], should be: ['non_speech', 'speech'])

    To Reproduce Steps to reproduce the behavior:

    $ export EXP_DIR=tutorials/pipelines/speaker_diarization 
    $ pyannote-audio scd  apply --step=0.1 --pretrained="<path to>/tutorials/models/speaker_change_detection/train/myData.SpeakerDiarization.general.train/validate_segmentation_fscore/myData.SpeakerDiarization.general.train" --subset=train ${EXP_DIR} myData.SpeakerDiarization.general
    
    

    pyannote environment

    $ pip freeze | grep pyannote
    pyannote.core==4.1
    pyannote.database==4.0.1
    pyannote.metrics==3.0.1
    pyannote.pipeline==1.5.2
    

    Additional context I only prepared a development set called "train" right now - so I'm running on that. I successfully ran the SAD apply step before moving to SCD.

    wontfix 
    opened by danFromTelAviv 20
  • An error was encountered while loading

    An error was encountered while loading "pyannote/speaker-diarization"

    Hello,when i run the code :

    from pyannote.audio import Pipeline
    pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization",
                                        use_auth_token="my_token")
    

    I get an error :

    Traceback (most recent call last):
      File "/home/dg/anaconda3/envs/pyannote/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py", line 213, in hf_raise_for_status
        response.raise_for_status()
      File "/home/dg/anaconda3/envs/pyannote/lib/python3.8/site-packages/requests/models.py", line 1021, in raise_for_status
        raise HTTPError(http_error_msg, response=self)
    requests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://huggingface.co/pyannote/segmentation/resolve/2022.07/pytorch_model.bin
    

    whether I use the read token role or the write token role. Anyone else know how to fix it? Thx.

    opened by Zpadger 18
  • [WIP] Feat/vtc

    [WIP] Feat/vtc

    This is a working PR on the future VTC implementation inspired from @MarvinLvn 's work, and to be merged into the next release of pyannote-audio.

    Note: nothing has been done yet, this is just to get things started.

    wontfix 
    opened by hadware 17
  • Trying to finetune model for new speaker

    Trying to finetune model for new speaker

    I am trying to finetune models to support one more speaker, but it looks I am doing something wrong.

    I want to use "dia_hard" pipeline, so I need to finetune models: {sad_dihard, scd_dihard, emb_voxceleb}.

    For my speaker I have one WAV file with duration more then 1 hour.

    So, I created database.yml file:

    Databases:
       IK: /content/fine/kirilov/{uri}.wav
    
    Protocols:
        IK:
           SpeakerDiarization:
              kirilov:
                train:
                   uri: train.lst
                   annotation: train.rttm
                   annotated: train.uem
    

    and put additional files near database.yml:

    kirilov
    ├── database.yml
    ├── kirilov.wav
    ├── train.lst
    ├── train.rttm
    └── train.uem
    

    train.lst: kirilov

    train.rttm: SPEAKER kirilov 1 0.0 3600.0 <NA> <NA> Kirilov <NA> <NA>

    train.uem: kirilov NA 0.0 3600.0

    I assume it will say trainer to use kirilov.wav file and take 3600 seconds of audio from it to use for training.

    Now I finetune the models, current folder is /content/fine/kirilov, so database.yml is taken from the current directory:

    !pyannote-audio sad train --pretrained=sad_dihard --subset=train --to=1 --parallel=4 "/content/fine/sad" IK.SpeakerDiarization.kirilov
    !pyannote-audio scd train --pretrained=scd_dihard --subset=train --to=1 --parallel=4 "/content/fine/scd" IK.SpeakerDiarization.kirilov
    !pyannote-audio emb train --pretrained=emb_voxceleb --subset=train --to=1 --parallel=4 "/content/fine/emb" IK.SpeakerDiarization.kirilov
    

    Output looks like:

    Using cache found in /root/.cache/torch/hub/pyannote_pyannote-audio_develop
    Loading labels: 0file [00:00, ?file/s]/usr/local/lib/python3.6/dist-packages/pyannote/database/protocol/protocol.py:128: UserWarning:
    
    Existing key "annotation" may have been modified.
    
    Loading labels: 1file [00:00, 20.49file/s]
    /usr/local/lib/python3.6/dist-packages/pyannote/audio/train/trainer.py:128: UserWarning:
    
    Did not load optimizer state (most likely because current training session uses a different loss than the one used for pre-training).
    
    2020-06-19 15:35:26.763592: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
    Training:   0%|                                        | 0/1 [00:00<?, ?epoch/s]
    Epoch pyannote/pyannote-database#1:   0%|                                       | 0/29 [00:00<?, ?batch/s]
    Epoch pyannote/pyannote-database#1:   0%|                           | 0/29 [00:00<?, ?batch/s, loss=0.676]
    Epoch pyannote/pyannote-database#1:   3%|▋                  | 1/29 [00:00<00:26,  1.04batch/s, loss=0.676]
    

    Etc.

    And try to run pipeline with new .pt's:

    import os
    import torch
    from pyannote.audio.pipeline import SpeakerDiarization
    pipeline = SpeakerDiarization(embedding = "/content/fine/emb/train/IK.SpeakerDiarization.kirilov.train/weights/0001.pt", 
                                  sad_scores = "/content/fine/sad/train/IK.SpeakerDiarization.kirilov.train/weights/0001.pt",
                                  scd_scores = "/content/fine/scd/train/IK.SpeakerDiarization.kirilov.train/weights/0001.pt",
                                  method= "affinity_propagation")
    
    #params from dia_dihard\train\X.SpeakerDiarization.DIHARD_Official.development\params.yml
    pipeline.load_params("/content/drive/My Drive/pyannote/params.yml")
    FILE = {'audio': "/content/groundtruth/new.wav"}
    diarization = pipeline(FILE)
    diarization
    

    The result is that for my new.wav the whole audio is recognized as speaker talking without pauses. So I assume that the models were broken. And it does not matter if I train for 1 epoch or for 100.

    In case I use:

    1. 0000.pt - I assume these are the original models
    pipeline = SpeakerDiarization(embedding = "/content/fine/emb/train/IK.SpeakerDiarization.kirilov.train/weights/0000.pt", 
                                  sad_scores = "/content/fine/sad/train/IK.SpeakerDiarization.kirilov.train/weights/0000.pt",
                                  scd_scores = "/content/fine/scd/train/IK.SpeakerDiarization.kirilov.train/weights/0000.pt",
                                  method= "affinity_propagation")
    

    or

    1. weights from original models
    pipeline = SpeakerDiarization(embedding = "/content/drive/My Drive/pyannote/emb_voxceleb/train/X.SpeakerDiarization.VoxCeleb.train/weights/0326.pt", 
                                 sad_scores = "/content/drive/My Drive/pyannote/sad_dihard/sad_dihard/train/X.SpeakerDiarization.DIHARD_Official.train/weights/0231.pt",
                                 scd_scores = "/content/drive/My Drive/pyannote/scd_dihard/train/X.SpeakerDiarization.DIHARD_Official.train/weights/0421.pt",
                                 method= "affinity_propagation")
    

    everything is ok and the result is similar to

    pipeline = torch.hub.load('pyannote/pyannote-audio', 'dia_dihard')
    FILE = {'audio': "/content/groundtruth/new.wav"}
    diarization = pipeline(FILE)
    diarization
    

    Could you please advise what could be wrong with my training\finetuning process?

    opened by marlon-br 17
  • `b c t` vs. `b t c`?

    `b c t` vs. `b t c`?

    Issue by hbredin Friday Oct 30, 2020 at 16:46 GMT Originally opened as https://github.com/hbredin/pyannote-audio-v2/issues/54


    Which convention should we use?

    v2 
    opened by mogwai 16
  • Segmentation Fault when conducting change detection tutorial

    Segmentation Fault when conducting change detection tutorial

    Hi,

    Everything seems ok for the feature extraction tutorial. But when I train the model following exactly what the tutorial asks me to do for change detection, I got segmentation fault. What might be probably the reason. Thank you for your help.

    opened by Charliechen1 16
  • Cannot find my Pretrained model

    Cannot find my Pretrained model

    I successfully trained an sad model. I want to create sad scores as part of the speaker diarization pipeline. I thought I am passing the weights correctly to the pyannote-audio script but the model is never found and the script aborts. Here is the output of my bash script with tracing on.

    This is the error message I get.

    RuntimeError: Cannot find callable /misc/vlgscratch4/PichenyGroup/picheny/headcam/headcam-code-try2/models/speech_activity_detection/train/AMI.SpeakerDiarization.MixHeadset.train/weights/0101.pt in hubconf

    For readability, I have boldfaced the commands in the script.

    ++ export EXP_DIR=models/speaker_diarization ++ EXP_DIR=models/speaker_diarization ++ cd /misc/vlgscratch4/PichenyGroup/picheny/headcam/headcam-code-try2 ++ export PYANNOTE_DATABASE_CONFIG=/misc/vlgscratch4/PichenyGroup/picheny/headcam/headcam-code-try2/database.yml ++ PYANNOTE_DATABASE_CONFIG=/misc/vlgscratch4/PichenyGroup/picheny/headcam/headcam-code-try2/database.yml ++ sad_model=/misc/vlgscratch4/PichenyGroup/picheny/headcam/headcam-code-try2/models/speech_activity_detection/train/AMI.SpeakerDiarization.MixHeadset.train/weights/0101.pt ++ ls /misc/vlgscratch4/PichenyGroup/picheny/headcam/headcam-code-try2/models/speech_activity_detection/train/AMI.SpeakerDiarization.MixHeadset.train/weights/0101.pt /misc/vlgscratch4/PichenyGroup/picheny/headcam/headcam-code-try2/models/speech_activity_detection/train/AMI.SpeakerDiarization.MixHeadset.train/weights/0101.pt ++ pyannote-audio sad apply --step=0.1 --pretrained=/misc/vlgscratch4/PichenyGroup/picheny/headcam/headcam-code-try2/models/speech_activity_detection/train/AMI.SpeakerDiarization.MixHeadset.train/weights/0101.pt --subset=dev models/speaker_diarization AMI.SpeakerDiarization.MixHeadset Using cache found in /home/map22/.cache/torch/hub/pyannote_pyannote-audio_develop Traceback (most recent call last): File "/misc/vlgscratch4/PichenyGroup/picheny/anaconda3/envs/pyannote-feb32020/bin/pyannote-audio", line 8, in sys.exit(main()) File "/misc/vlgscratch4/PichenyGroup/picheny/anaconda3/envs/pyannote-feb32020/lib/python3.7/site-packages/pyannote/audio/applications/pyannote_audio.py", line 406, in main apply_pretrained(validate_dir, protocol, **params) File "/misc/vlgscratch4/PichenyGroup/picheny/anaconda3/envs/pyannote-feb32020/lib/python3.7/site-packages/pyannote/audio/applications/base.py", line 514, in apply_pretrained step=step) File "/misc/vlgscratch4/PichenyGroup/picheny/anaconda3/envs/pyannote-feb32020/lib/python3.7/site-packages/torch/hub.py", line 364, in load entry = _load_entry_from_hubconf(hub_module, model) File "/misc/vlgscratch4/PichenyGroup/picheny/anaconda3/envs/pyannote-feb32020/lib/python3.7/site-packages/torch/hub.py", line 237, in _load_entry_from_hubconf raise RuntimeError('Cannot find callable {} in hubconf'.format(model)) RuntimeError: Cannot find callable /misc/vlgscratch4/PichenyGroup/picheny/headcam/headcam-code-try2/models/speech_activity_detection/train/AMI.SpeakerDiarization.MixHeadset.train/weights/0101.pt in hubconf

    opened by picheny-nyu 15
  • TypeError: __init__() missing 1 required positional argument: 'signature' while reproducing change-detection tutorial

    TypeError: __init__() missing 1 required positional argument: 'signature' while reproducing change-detection tutorial

    While following this tutorial https://github.com/pyannote/pyannote-audio/tree/89da05ea9d6de97da9bd21949a26ceb0042ef361/tutorials/change-detection

    and while executing this " pyannote-change-detection train \ ${EXPERIMENT_DIR} \ AMI.SpeakerDiarization.MixHeadset "

    File "/home/jashwanth/miniconda3/envs/py36-pyannote-audio/bin/pyannote-change-detection", line 8, in sys.exit(main()) File "/home/jashwanth/miniconda3/envs/py36-pyannote-audio/lib/python3.6/site-packages/pyannote/audio/applications/change_detection.py", line 380, in main train(protocol, experiment_dir, train_dir, subset=subset) File "/home/jashwanth/miniconda3/envs/py36-pyannote-audio/lib/python3.6/site-packages/pyannote/audio/applications/change_detection.py", line 202, in train generator = ChangeDetectionBatchGenerator(feature_extraction) File "/home/jashwanth/miniconda3/envs/py36-pyannote-audio/lib/python3.6/site-packages/pyannote/audio/generators/change.py", line 93, in init segment_generator) TypeError: init() missing 1 required positional argument: 'signature' please can anyone help me with this!!

    Thank you in advance

    opened by Jashwantherao 0
  • How to select threshold and min_cluster_size values in clustering after I finetuned embedding model ?

    How to select threshold and min_cluster_size values in clustering after I finetuned embedding model ?

    Hi. Thanks for sharing great repository. I have a question. I finetuned embedding model in speaker diarization pipeline. After that, I don't know how to set threshold and min_cluster_size params in config.yaml file. Can you give me some advices ?

    opened by dungnguyen98 0
  • Fixes for PytorchLightning >= 1.8

    Fixes for PytorchLightning >= 1.8

    Adjust to PytorchLightning API changes in version 1.8.0. I did some testing to make sure nothing broke, including model training/finetuning and loading from pretrained/HF-hub; however, my tests likely didn't cover everything.

    opened by entn-at 1
  • Support MLflow along with Tensorboard for logging segmentation task visualizations during validation

    Support MLflow along with Tensorboard for logging segmentation task visualizations during validation

    When using PyTorch-Lightning's MLFlowLogger instead of TensorBoardLogger during training of segmentation models, the current implementation fails because MLflow's experiment tracking client has a different method for logging figures than Tensorboard. Unfortunately, PyTorch-Lightning doesn't abstract away this logger API difference.

    Tested with MLflow and Tensorboard.

    opened by entn-at 1
  • Will it work on real time streaming data ?

    Will it work on real time streaming data ?

    I am currently running it on Apple M2 chip it is taking way much time comparing to Colab. Is there a way that pipeline could be modified to streaming data, and combined with some transcription service ?

    opened by ankurdhuriya 0
Releases(1.1.1)
Unofficial Implementation of Zero-Shot Text-to-Speech for Text-Based Insertion in Audio Narration

Zero-Shot Text-to-Speech for Text-Based Insertion in Audio Narration This repo contains only model Implementation of Zero-Shot Text-to-Speech for Text

Rishikesh (ऋषिकेश) 33 Sep 22, 2022
Built for cleaning purposes in military institutions

Ferramenta do AL Construído para fins de limpeza em instituições militares. Instalação Requer python = 3.2 pip install -r requirements.txt Usagem Exe

0 Aug 13, 2022
Text-to-Speech for Belarusian language

title emoji colorFrom colorTo sdk app_file pinned Belarusian TTS 🐸 green green gradio app.py false Belarusian TTS 📢 🤖 Belarusian TTS (text-to-speec

Yurii Paniv 1 Nov 27, 2021
Implementation of "Adversarial purification with Score-based generative models", ICML 2021

Adversarial Purification with Score-based Generative Models by Jongmin Yoon, Sung Ju Hwang, Juho Lee This repository includes the official PyTorch imp

15 Dec 15, 2022
PyTranslator é simultaneamente um editor e tradutor de texto com diversos recursos e interface feito com coração e 100% em Python

PyTranslator O Que é e para que serve o PyTranslator? PyTranslator é simultaneamente um editor e tradutor de texto em com interface gráfica que usa a

Elizeu Barbosa Abreu 1 May 12, 2022
Sploitus - Command line search tool for sploitus.com. Think searchsploit, but with more POCs

Sploitus Command line search tool for sploitus.com. Think searchsploit, but with

watchdog2000 5 Mar 07, 2022
CCKS-Title-based-large-scale-commodity-entity-retrieval-top1

- 基于标题的大规模商品实体检索top1 一、任务介绍 CCKS 2020:基于标题的大规模商品实体检索,任务为对于给定的一个商品标题,参赛系统需要匹配到该标题在给定商品库中的对应商品实体。 输入:输入文件包括若干行商品标题。 输出:输出文本每一行包括此标题对应的商品实体,即给定知识库中商品 ID,

43 Nov 11, 2022
숭실대학교 컴퓨터학부 전공종합설계프로젝트

✨ 시각장애인을 위한 버스도착 알림 장치 ✨ 👀 개요 현대 사회에서 대중교통 위치 정보를 이용하여 사람들이 간단하게 이용할 대중교통의 정보를 얻고 쉽게 대중교통을 이용할 수 있다. 해당 정보는 각종 어플리케이션과 대중교통 이용시설에서 위치 정보를 제공하고 있지만 시각

taegyun 3 Jan 25, 2022
Rank-One Model Editing for Locating and Editing Factual Knowledge in GPT

Rank-One Model Editing (ROME) This repository provides an implementation of Rank-One Model Editing (ROME) on auto-regressive transformers (GPU-only).

Kevin Meng 130 Dec 21, 2022
Finally, some decent sample sentences

tts-dataset-prompts This repository aims to be a decent set of sentences for people looking to clone their own voices (e.g. using Tacotron 2). Each se

hecko 19 Dec 13, 2022
A model library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing neural networks

A Deep Learning NLP/NLU library by Intel® AI Lab Overview | Models | Installation | Examples | Documentation | Tutorials | Contributing NLP Architect

Intel Labs 2.9k Dec 31, 2022
Neural-Machine-Translation - Implementation of revolutionary machine translation models

Neural Machine Translation Framework: PyTorch Repository contaning my implementa

Utkarsh Jain 1 Feb 17, 2022
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering

Disfl-QA is a targeted dataset for contextual disfluencies in an information seeking setting, namely question answering over Wikipedia passages. Disfl-QA builds upon the SQuAD-v2 (Rajpurkar et al., 2

Google Research Datasets 52 Jun 21, 2022
PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

Microsoft 105 Jan 08, 2022
Yet Another Compiler Visualizer

yacv: Yet Another Compiler Visualizer yacv is a tool for visualizing various aspects of typical LL(1) and LR parsers. Check out demo on YouTube to see

Ashutosh Sathe 129 Dec 17, 2022
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

Pattern Pattern is a web mining module for Python. It has tools for: Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM par

Computational Linguistics Research Group 8.4k Dec 30, 2022
jel - Japanese Entity Linker - is Bi-encoder based entity linker for japanese.

jel: Japanese Entity Linker jel - Japanese Entity Linker - is Bi-encoder based entity linker for japanese. Usage Currently, link and question methods

izuna385 10 Jan 06, 2023
Python powered crossword generator with database with 20k+ polish words

crossword_generator Generate simple crossword puzzle from words and definitions fetched from krzyżowki.edu.pl endpoints -/ string:word - returns js

0 Jan 04, 2022
Segmenter - Transformer for Semantic Segmentation

Segmenter - Transformer for Semantic Segmentation

592 Dec 27, 2022
Code to reproduce the results of the paper 'Towards Realistic Few-Shot Relation Extraction' (EMNLP 2021)

Realistic Few-Shot Relation Extraction This repository contains code to reproduce the results in the paper "Towards Realistic Few-Shot Relation Extrac

Bloomberg 8 Nov 09, 2022